code: def init_batch(
      self, context_tokens: torch.Tensor, context_length: int
  ):
      self.retrieved = []
      self.retrieved_text = []
      """initialize the batch data before the inference steps."""
      # Move to GPU.
      tokenizer = self.model.tokenizer
      tokens = context_tokens.contiguous().cuda()
      micro_batch_size, seq_length = tokens.size()
      position_ids = torch.arange(
          seq_length, dtype=torch.long, device=tokens.device
      )
      self.position_ids = position_ids.unsqueeze(0).repeat(
          micro_batch_size, 1
      )
      if self.megatron_lm_compatible:
          # all TRUE for megatron lm, there is no attention mask
          self.attention_mask = torch.ones_like(
              tokens, dtype=torch.bool
          )
      else:
          self.attention_mask = tokens != tokenizer.pad_id
      for i in range(0, context_length, 64):
          if i > 0:
              tokens = context_tokens[:, i - 64 : i]
              chunks = self.service.get_knn(tokens, self.neighbors)
              if self.store_retrieved:
                  self._store_retrieved(tokens, chunks)
              self.retrieved.append(chunks)
  
imports: import json
  import pickle
  from typing import List, Tuple
  import numpy
  import torch
  import torch.distributed
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.retrieval_service import ComboRetrievalService
  from nemo.collections.nlp.modules.common.text_generation_strategy import TextGenerationStrategy
label_desc: Use image node_id 8 for calling the RetroModelTextGenerationStrategy obj's underlying member method code with example usage: obj.init_batch(context_tokens, context_length) without return types